Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic Tomography
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arXiv
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| Format: | Preprint |
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2024
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| author | Zhong, Yutian Zhang, Xiaoming Mo, Zongxin Zhang, Shuangyang Chen, Wufan Qi, Li |
| author_facet | Zhong, Yutian Zhang, Xiaoming Mo, Zongxin Zhang, Shuangyang Chen, Wufan Qi, Li |
| contents | Multispectral photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect to achieve non-invasive and high-contrast imaging of internal tissues. However, the hardware cost and computational demand of a multispectral PAT system consisting of up to thousands of detectors are huge. To address this challenge, we propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT. Our strategy employs a sparse ring-shaped transducer that, when switching excitation wavelengths, simultaneously rotates and translates. This creates a spiral scanning pattern with multispectral angle-interlaced sampling. To solve the highly ill-conditioned image reconstruction problem, we propose a self-supervised learning method that is able to introduce structural information shared during spiral scanning. We simulate the proposed U3S-PAT method on a commercial PAT system and conduct in vivo animal experiments to verify its performance. The results show that even with a sparse sampling rate as low as 1/30, our U3S-PAT strategy achieves similar reconstruction and spectral unmixing accuracy as non-spiral dense sampling. Given its ability to dramatically reduce the time required for three-dimensional multispectral scanning, our U3S-PAT strategy has the potential to perform volumetric molecular imaging of dynamic biological activities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06695 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic Tomography Zhong, Yutian Zhang, Xiaoming Mo, Zongxin Zhang, Shuangyang Chen, Wufan Qi, Li Image and Video Processing Medical Physics Multispectral photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect to achieve non-invasive and high-contrast imaging of internal tissues. However, the hardware cost and computational demand of a multispectral PAT system consisting of up to thousands of detectors are huge. To address this challenge, we propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT. Our strategy employs a sparse ring-shaped transducer that, when switching excitation wavelengths, simultaneously rotates and translates. This creates a spiral scanning pattern with multispectral angle-interlaced sampling. To solve the highly ill-conditioned image reconstruction problem, we propose a self-supervised learning method that is able to introduce structural information shared during spiral scanning. We simulate the proposed U3S-PAT method on a commercial PAT system and conduct in vivo animal experiments to verify its performance. The results show that even with a sparse sampling rate as low as 1/30, our U3S-PAT strategy achieves similar reconstruction and spectral unmixing accuracy as non-spiral dense sampling. Given its ability to dramatically reduce the time required for three-dimensional multispectral scanning, our U3S-PAT strategy has the potential to perform volumetric molecular imaging of dynamic biological activities. |
| title | Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic Tomography |
| topic | Image and Video Processing Medical Physics |
| url | https://arxiv.org/abs/2404.06695 |